Performance Improvement in Unfalsified Control Using Neural Networks
نویسندگان
چکیده
Abstract: In this paper, a novel combination of unfalsified control and intelligent control is proposed to improve the dynamic performance of an uncertain system. A PID controller, whose parameters are adaptively tuned by switching among members of a given candidate set using observed plant data, is presented and compared with multi-model adaptive control. Two different cost functions are compared for their capability in selecting the “best” controller. The principle of Radial Basis Function Neural Networks (RBFNN) is used to update the parameters of the selected PID controller to further improve the performance. Simulation results demonstrate that the proposed control switching strategy compares favorably to the alternatives.
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تاریخ انتشار 2008